OpenRaiser/Intern-Atlas huggingface dataset:
Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application
Venue: arXiv cs.CV 2020
Year: 2020

A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.
An Overview of Two Age Synthesis and Estimation Techniques
Venue: arXiv cs.CV 2020
Year: 2020

Age estimation is a technique for predicting human ages from digital facial images, which analyzes a person's face image and estimates his/her age based on the year measure. Nowadays, intelligent age estimation and age synthesis have become particularly prevalent research topics in computer vision and face verification systems. Age synthesis is defined to render a facial image aesthetically with rejuvenating and natural aging effects on the person's face. Age estimation is defined to label a facial image automatically with the age group (year range) or the exact age (year) of the person's face. In this case study, we overview the existing models, popular techniques, system performances, and technical challenges related to the facial image-based age synthesis and estimation topics. The main goal of this review is to provide an easy understanding and promising future directions with systematic discussions.
Learning Numerical Observers using Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2020
Year: 2020

Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. However, labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors. Computer-simulated image data can potentially be employed to circumvent these issues; however, it is often difficult to computationally model complicated anatomical structures, noise sources, and the response of real world imaging systems. Hence, simulated image data will generally possess physical and statistical differences from the experimental image data they seek to emulate. Within the context of machine learning, these differences between the sets of two images is referred to as domain shift. In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones. In the proposed method, a DL-NO will initially be trained on computer-simulated image data and subsequently adapted for use with experimental image data, without the need for any labeled experimental images. As a proof of concept, a binary signal detection task is considered. The success of this strategy as a function of the degree of domain shift present between the simulated and experimental image data is investigated.
Engineering and Experimentally Benchmarking a Container-based Edge Computing System
Venue: arXiv cs.NI 2020
Year: 2020

While edge computing is envisioned to superbly serve latency sensitive applications, the implementation-based studies benchmarking its performance are few and far between. To address this gap, we engineer a modular edge cloud computing system architecture that is built on latest advances in containerization techniques, including Kafka, for data streaming, Docker, as application platform, and Firebase Cloud, as realtime database system. We benchmark the performance of the system in terms of scalability, resource utilization and latency by comparing three scenarios: cloud-only, edge-only and combined edge-cloud. The measurements show that edge-only solution outperforms other scenarios only when deployed with data located at one edge only, i.e., without edge computing wide data synchronization. In case of applications requiring data synchronization through the cloud, edge-cloud scales around a factor 10 times better than cloud-only, until certain number of concurrent users in the system, and above this point, cloud-only scales better. In terms of resource utilization, we observe that whereas the mean utilization increases linearly with the number of user requests, the maximum values for the memory and the network I/O heavily increase when with an increasing amount of data.
Dynamic Multi-Modulation Allocation Scheme for Elastic Optical Networks
Venue: arXiv cs.NI 2020
Year: 2020

In order to deal with the recent rapid increase in Internet traffic, a transmission technology is required to enable the efficient use of the optical fiber spectrum while offering flexibility in network bandwidth. To meet these challenges, the emergence of Elastic Optical Networks (EON) has brought new conceptions in the operation of optical networks, improving flexibility and efficiency for the next generation core networks. In EON, traffic demands are typically supported by allocating bandwidth-variable optical channels with heterogeneous modulation formats in a spectral-efficient manner. Elastic optical path networks require the routing, modulation level, and spectrum allocation (RMLSA) to efficiently allocate optical spectrum resources to optical paths. To address the RMLSA problem, Modulation Scheme approaches have recently been proposed to allow the use of any routing and spectrum assignment (RSA) algorithm to solve the RMLSA problem. In this paper, we propose a new Modulation Scheme that enables the routing of traffic through dynamic multi-modulation allocation in multiple hops to achieve blocking performance improvement. Numerical results demonstrate that the proposed adaptive modulation scheme achieves a reduction in bandwidth blocking of up to two orders of magnitude in an underloaded network scenario, and 86% with higher loads, playing an important role in spectrum savings compared with the literature schemes.
Making Logic Learnable With Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally verifiable. On the other hand, logic circuits are implementable, verifiable, and interpretable but are not able to learn from training data in a generalizable way. We propose a novel logic learning pipeline that combines the advantages of neural networks and logic circuits. Our pipeline first trains a neural network on a classification task, and then translates this, first to random forests, and then to AND-Inverter logic. We show that our pipeline maintains greater accuracy than naive translations to logic, and minimizes the logic such that it is more interpretable and has decreased hardware cost. We show the utility of our pipeline on a network that is trained on biomedical data. This approach could be applied to patient care to provide risk stratification and guide clinical decision-making.
Beyond Trolling: Malware-Induced Misperception Attacks on Polarized Facebook Discourse
Venue: arXiv cs.HC 2020
Year: 2020

Social media trolling is a powerful tactic to manipulate public opinion on issues with a high moral component. Troll farms, as evidenced in the past, created fabricated content to provoke or silence people to share their opinion on social media during the US presidential election in 2016. In this paper, we introduce an alternate way of provoking or silencing social media discourse by manipulating how users perceive authentic content. This manipulation is performed by man-in-the-middle malware that covertly rearranges the linguistic content of an authentic social media post and comments. We call this attack Malware-Induced Misperception (MIM) because the goal is to socially engineer spiral-of-silence conditions on social media by inducing perception. We conducted experimental tests in controlled settings (N = 311) where a malware covertly altered selected words in a Facebook post about the freedom of political expression on college campuses. The empirical results (1) confirm the previous findings about the presence of the spiral-of-silence effect on social media; and (2) demonstrate that inducing misperception is an effective tactic to silence or provoke targeted users on Facebook to express their opinion on a polarizing political issue.
Edge Matching with Inequalities, Triangles, Unknown Shape, and Two Players
Venue: arXiv cs.CC 2020
Year: 2020

We analyze the computational complexity of several new variants of edge-matching puzzles. First we analyze inequality (instead of equality) constraints between adjacent tiles, proving the problem NP-complete for strict inequalities but polynomial for nonstrict inequalities. Second we analyze three types of triangular edge matching, of which one is polynomial and the other two are NP-complete; all three are #P-complete. Third we analyze the case where no target shape is specified, and we merely want to place the (square) tiles so that edges match (exactly); this problem is NP-complete. Fourth we consider four 2-player games based on $1 \times n$ edge matching, all four of which are PSPACE-complete. Most of our NP-hardness reductions are parsimonious, newly proving #P and ASP-completeness for, e.g., $1 \times n$ edge matching.
Novel Machine Learning Algorithms for Centrality and Cliques Detection in Youtube Social Networks
Venue: arXiv cs.LG 2020
Year: 2020

The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

We propose a novel efficient and lightweight model for human pose estimation from a single image. Our model is designed to achieve competitive results at a fraction of the number of parameters and computational cost of various state-of-the-art methods. To this end, we explicitly incorporate part-based structural and geometric priors in a hierarchical prediction framework. At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts. Unlike classical approaches, we adopt end-to-end training to learn this geometric prior through feature updates from data. We then propagate the feature representation at the coarsest resolution up the hierarchy to refine the predicted pose in a coarse-to-fine fashion. The final network effectively models the geometric prior and intuition within a lightweight deep neural network, yielding state-of-the-art results for a model of this size on two standard datasets, Leeds Sports Pose and MPII Human Pose.
Adaptive Online Learning with Varying Norms
Venue: arXiv cs.LG 2020
Year: 2020

Given any increasing sequence of norms $\|\cdot\|_0,\dots,\|\cdot\|_{T-1}$, we provide an online convex optimization algorithm that outputs points $w_t$ in some domain $W$ in response to convex losses $\ell_t:W\to \mathbb{R}$ that guarantees regret $R_T(u)=\sum_{t=1}^T \ell_t(w_t)-\ell_t(u)\le \tilde O\left(\|u\|_{T-1}\sqrt{\sum_{t=1}^T \|g_t\|_{t-1,\star}^2}\right)$ where $g_t$ is a subgradient of $\ell_t$ at $w_t$. Our method does not require tuning to the value of $u$ and allows for arbitrary convex $W$. We apply this result to obtain new "full-matrix"-style regret bounds. Along the way, we provide a new examination of the full-matrix AdaGrad algorithm, suggesting a better learning rate value that improves significantly upon prior analysis. We use our new techniques to tune AdaGrad on-the-fly, realizing our improved bound in a concrete algorithm.
Palindromic k-Factorization in Pure Linear Time
Venue: arXiv cs.DS 2020
Year: 2020

Given a string $s$ of length $n$ over a general alphabet and an integer $k$, the problem is to decide whether $s$ is a concatenation of $k$ nonempty palindromes. Two previously known solutions for this problem work in time $O(kn)$ and $O(n\log n)$ respectively. Here we settle the complexity of this problem in the word-RAM model, presenting an $O(n)$-time online deciding algorithm. The algorithm simultaneously finds the minimum odd number of factors and the minimum even number of factors in a factorization of a string into nonempty palindromes. We also demonstrate how to get an explicit factorization of $s$ into $k$ palindromes with an $O(n)$-time offline postprocessing.
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
Venue: arXiv cs.LG 2020
Year: 2020

Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of such variables can degrade the performance of deep-learning models. We study three datasets where there is a strong influence of known extraneous variables: classification of upper-body movements in stroke patients, annotation of surgical activities, and recognition of corrupted images. Models trained with batch normalization learn features that are highly dependent on the extraneous variables. In batch normalization, the statistics used to normalize the features are learned from the training set and fixed at test time, which produces a mismatch in the presence of varying extraneous variables. We demonstrate that estimating the feature statistics adaptively during inference, as in instance normalization, addresses this issue, producing normalized features that are more robust to changes in the extraneous variables. This results in a significant gain in performance for different network architectures and choices of feature statistics.
Unsupervised Learning of Audio Perception for Robotics Applications: Learning to Project Data to T-SNE/UMAP space
Venue: arXiv cs.SD 2020
Year: 2020

Audio perception is a key to solving a variety of problems ranging from acoustic scene analysis, music meta-data extraction, recommendation, synthesis and analysis. It can potentially also augment computers in doing tasks that humans do effortlessly in day-to-day activities. This paper builds upon key ideas to build perception of touch sounds without access to any ground-truth data. We show how we can leverage ideas from classical signal processing to get large amounts of data of any sound of interest with a high precision. These sounds are then used, along with the images to map the sounds to a clustered space of the latent representation of these images. This approach, not only allows us to learn semantic representation of the possible sounds of interest, but also allows association of different modalities to the learned distinctions. The model trained to map sounds to this clustered representation, gives reasonable performance as opposed to expensive methods collecting a lot of human annotated data. Such approaches can be used to build a state of art perceptual model for any sound of interest described using a few signal processing features. Daisy chaining high precision sound event detectors using signal processing combined with neural architectures and high dimensional clustering of unlabelled data is a vastly powerful idea, and can be explored in a variety of ways in future.
Towards a distributed and infrastructure-less vehicular traffic management system
Venue: arXiv cs.NI 2020
Year: 2020

In the past few years, several systems have been proposed to deal with issues related to the vehicular traffic management. Usually, their solutions include the integration of computational technologies such as vehicular networks, central servers, and roadside units. Most systems use a hybrid approach, which means they still need a central entity (central server or roadside unit) and Internet connection to find out an en-route event as well as alternative routes for vehicles. It is easy to understand the need for a central entity because selecting the most appropriate vehicle to perform aforementioned procedures is a difficult task. In addition to that, as far as we know, there are very few systems that apply the altruistic approach (not selfish behavior) to routing decisions. Because of that, the issue addressed in this work is how to perform the vehicular traffic management, when an en-route event is detected, in a distributed, scalable, and cost-effective fashion. To deal with these issues, we proposed a distributed vehicle traffic management system, named as dEASY (distributed vEhicle trAffic management SYstem). The dEASY system was designed and implemented on a three-layer architecture, namely environment sensing and vehicle ranking, knowledge generation and distribution, and knowledge consumption. Each layer of the dEASY architecture is responsible for dealing with the main issues that were not addressed in related works or could be improved. Simulation results have shown that, compared with other systems from the literature, our proposed system has lower network overhead due to applied vehicle selection and broadcast suppression mechanisms. In average, dEASY also outperformed all other competitors in what regards to the travel time and time lost metrics. Through the analysis of results, it is possible to conclude that our infrastructure-less system is scalable and cost-effective.
Dynamic Impact for Ant Colony Optimization algorithm
Venue: arXiv cs.NE 2020
Year: 2020

This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to solve challenging optimization problems that has nonlinear relationship between resource consumption and fitness in relation to other part of the optimized solution. This proposed method is tested against complex real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem, as well as theoretical benchmark Multi-Dimensional Knapsack problem (MKP). MMPPFO is a non-trivial optimization problem, due the nature of solution fitness value dependence on collection of wafer-lots without prioritization of any individual wafer-lot. Using Dynamic Impact on single objective optimization fitness value is improved by 33.2%. Furthermore, MKP benchmark instances of small complexity have been solved to 100% success rate where high degree of solution sparseness is observed, and large instances have showed average gap improved by 4.26 times. Algorithm implementation demonstrated superior performance across small and large datasets and sparse optimization problems.
Cybersecurity for Industrial Control Systems: A Survey
Venue: arXiv cs.CR 2020
Year: 2020

Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.
Thermal to Visible Face Recognition Using Deep Autoencoders
Venue: arXiv cs.CV 2020
Year: 2020

Visible face recognition systems achieve nearly perfect recognition accuracies using deep learning. However, in lack of light, these systems perform poorly. A way to deal with this problem is thermal to visible cross-domain face matching. This is a desired technology because of its usefulness in night time surveillance. Nevertheless, due to differences between two domains, it is a very challenging face recognition problem. In this paper, we present a deep autoencoder based system to learn the mapping between visible and thermal face images. Also, we assess the impact of alignment in thermal to visible face recognition. For this purpose, we manually annotate the facial landmarks on the Carl and EURECOM datasets. The proposed approach is extensively tested on three publicly available datasets: Carl, UND-X1, and EURECOM. Experimental results show that the proposed approach improves the state-of-the-art significantly. We observe that alignment increases the performance by around 2%. Annotated facial landmark positions in this study can be downloaded from the following link: github.com/Alpkant/Thermal-to-Visible-Face-Recognition-Using-Deep-Autoencoders .